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We propose a dynamic allocation procedure that increases power and efficiencywhen measuring an average treatment effect in sequential randomized trialsexploiting some subjects' previous assessed responses. Subjects arrivesequentially and are either randomized or paired to a previously randomizedsubject and administered the alternate treatment. The pairing is made via adynamic matching criterion that iteratively learns which specific covariatesare important to the response. We develop estimators for the average treatmenteffect as well as an exact test. We illustrate our method's increase inefficiency and power over other allocation procedures in both simulatedscenarios and a clinical trial dataset. An R package "SeqExpMatch" for use bypractitioners is available.

A Matching Procedure for Sequential Experiments that Iteratively Learns which Covariates Improve Power